Automatic Alerting Method and System for Aerial Vehicle Target Tracking

ABSTRACT

This invention provides a system and method for automatically inferring when a target that is being tracked by an aerial vehicle is doing something significant (e.g. stopping suddenly, changing direction quickly or getting out of track view), and consequently alerting an operator. The alerting system also provides a classification of what the target is doing. It frees the operator from continuously monitoring the imagery stream so the operator can perform other tasks.

GOVERNMENT LICENSE RIGHTS

The U.S. Government may have certain rights in the present invention as provided for by the terms of Contract No. FA8650-04-C-7142 with the Defense Advanced Research Projects Agency.

BACKGROUND TECHNOLOGY

Aerial vehicles (AVs) are manned or unmanned aircraft that can be remotely piloted or self-piloted by an onboard computer system and can carry cameras, sensors, communications equipment, or other payloads. They have been used in a reconnaissance and intelligence-gathering role for many years. More recently, AVs have been developed for the purpose of surveillance and target tracking.

Autonomous surveillance and target tracking performed by AVs in either military or civilian environments is becoming an important aspect of intelligence-gathering. Typically, when a target is being tracked from aerial vehicles (e.g. an AV), human operators must closely monitor imagery streamed from the aircraft to assess target behavior and ensure that the target continues to be in view.

SUMMARY

This invention provides a system and method for automatically inferring when the target is doing something significant (e.g. stopping suddenly, changing direction quickly or getting out of track view) and alerting the operator. The alerting system also provides a classification of what the target is doing. It frees the operator from continuously monitoring the imagery stream so the operator can perform other tasks.

BRIEF DESCRIPTION OF THE DRAWINGS

Features of the present invention will become apparent to those skilled in the art from the following description with reference to the drawings. Understanding that the drawings depict only typical embodiments of the invention and are not therefore to be considered limiting in scope, the invention will be described with additional specificity and detail through the use of the accompanying drawings, in which:

FIG. 1 is a schematic diagram depicting a system for aerial tracking of a ground vehicle according to one embodiment of the invention.

FIG. 2 is a simplified block diagram of an entity arranged to implement aspects of the exemplary embodiment.

FIG. 3 is a flow chart depicting functions that can be carried out in accordance with the exemplary embodiment.

FIG. 4 is a diagram depicting an example of a sensor footprint.

FIG. 5 is a diagram depicting the variables needed to calculate an instantaneous track metric.

FIG. 6 depicts a forward-looking sensor footprint that has been normalized.

DETAILED DESCRIPTION

In the following detailed description, embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It is to be understood that other embodiments may be utilized without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense.

FIG. 1 is a simplified diagram depicting a system 100 for automatically tracking a target from an AV. As shown in FIG. 1, the system includes (1) an AV 112 equipped with at least one sensor 114, (3) a target 116 and (4) a remote operator 118. It should be understood that while remote operator 118 is shown in FIG. 1, remote operator 118 may be many miles away from AV 112 and target 116.

The AV 112 is an aircraft capable of being remotely piloted or self-piloted. AV 112 may carry cameras, sensors, communications equipment, or other payloads. AV 112 may be a hover-capable aerial vehicle or a fixed-wing aerial vehicle. Sensor 114 may be any device capable of imaging a target, such as a camera or radar. Target 116 may be anything being monitored by AV 112. For example, target 116 may be a ground-based vehicle, an air-based vehicle, or a person. To acquire a target, AV 112 typically sends images from sensor 114 to remote operator 118. Remote operator 118 then defines an area in the image as the target, and sends the target to AV 112.

Remote operator 118 may be any device capable of communicating with AV 112. In addition, remote operator 118 may be configured to remotely control AV 112. Remote operator 118 may be a device such as a desktop computer equipped with a joystick, laptop, or personal data assistant (“PDA”), for example.

Aspects of the present invention may be carried out by AV 112 and/or remote operator 118 (or any other entity capable of controlling AV 112). FIG. 2 depicts functional components that may be included in AV 112 and/or remote operator 118 to carry out various aspects of the invention. As shown in FIG. 2, the components include a communication interface 200, a processing unit 202, and data storage 206, all of which may be coupled together by a system bus, network, or other mechanism 210.

Communication interface 200 comprises a mechanism for communicating over an air interface, so as to facilitate communication between AV 112 and remote operator 118. Further, communication interface 200 may include one or more antennas to facilitate air interface communication.

Processing unit 202 comprises one or more general purpose processors (e.g., INTEL microprocessors) and/or one or more special purpose processors (e.g., digital signal processors). Data storage 204, in turn, comprises one or more volatile and/or non-volatile storage mechanisms, such as memory and/or disc-drive storage for instance, which may be integrated in whole or in part with processing unit 202.

As shown, data storage 204 includes program logic 206 and reference data 208. Program logic 206 comprises one or more logic modules (applications), and preferably includes machine language instructions executable by processing unit 204 to carry out various functions described herein, such as (1) identifying the coordinates of the footprint of sensor 114, (2) computing a normalized track metric as a function of how close the target is to the center of the footprint, (3) comparing the track metric to a threshold value, and (4) alerting remote operator 118 if the track metric (or its time derivatives) falls outside a threshold value. Reference data 208, in turn, may include data such as imaging data acquired by sensor 114.

FIG. 3 is a flow chart depicting automatically alerting a remote operator about the status of a vehicle being tracked by an AV in accordance with an embodiment of the invention. In particular, FIG. 3 depicts (1) identifying the coordinates of the footprint of sensor 114, (2) computing a normalized track metric as a function of how close the target is to the center of the footprint, (3) comparing the track metric to a threshold value, and (4) alerting remote operator 118 if the track metric (or its time derivatives) falls outside the threshold value.

As shown in FIG. 3, at step 302, AV 112 identifies the coordinates of the vertices and center of the footprint (i.e., the viewing window) of sensor 114. Examples of sensor footprints are depicted in FIG. 4. As shown in FIG. 4, AV 112 is equipped with forward and side looking sensors. Forward looking sensor footprint 402 includes vertices {a, b, c, d}. The center of footprint 402 is identified as {i}. Side-looking sensor footprint 404 includes vertices {e, f, g, h}. The center of side-looking sensor footprint is identified as {j}.

FIG. 6 depicts a forward-looking sensor footprint that has been normalized (i.e., displayed as a rectangle). As shown in FIG. 6, the footprint includes vertices {a, b, c, d}, center {i} midpoints {ad_(c), ab_(c), bc_(c), dc_(c)}, and angles

$\left\{ {\frac{\alpha_{v}}{2},\frac{\alpha_{h}}{2}} \right\},$

where α_(h) and α_(v) are the horizontal and vertical field of view angles for sensor 114.

Returning to FIG. 3, the coordinates of the vertices and center of the sensor footprint may be computed using the following data:

[α_(h), α_(v)], the horizontal and vertical field of view for sensor 114;

[θ, φ, ψ], the attitude angles of AV 112, where θ, is the pitch, φ is the roll, and ψ is the yaw. In this example climb requires a positive pitch, the right wing down is a positive roll and clockwise from the top of the vehicle is a positive yaw;

[θ_(c), φ_(c), ψ_(c)], the attitude angles of sensor 114, where θ, is the pitch, φ is the roll, and ψ the yaw. In this example, pitch is measured between 0 and 90 degrees measured from straight down. The Camera lookdown angle is (1−θ_(c)), the roll angle is positive right and the yaw angle is positive in the clockwise direction. Consequently, a forward facing sensor 114 has ψ_(c)=0, while a left-pointing camera has a ψ_(c)=−90 degrees; and

[N, E, h], the position coordinates of AV 112 where N=north, E=east, and h=height from some reference point (such as UTM northings, eastings and altitude).

The local coordinates of the vertices and center of the footprint are identified as follows:

$\begin{bmatrix} a \\ b \\ c \\ d \\ i \end{bmatrix} = \begin{bmatrix} {\tan \left( \frac{\alpha_{v}}{2} \right)} & {- {\tan \left( \frac{\alpha_{h}}{2} \right)}} & 1 \\ {\tan \left( \frac{\alpha_{v}}{2} \right)} & {\tan \left( \frac{\alpha_{h}}{2} \right)} & 1 \\ {- {\tan \left( \frac{\alpha_{v}}{2} \right)}} & {\tan \left( \frac{\alpha_{h}}{2} \right)} & 1 \\ {- {\tan \left( \frac{\alpha_{v}}{2} \right)}} & {- {\tan \left( \frac{\alpha_{h}}{2} \right)}} & 1 \\ 0 & 0 & 1 \end{bmatrix}$

At step 304, the local coordinates of the midpoints for each side of the sensor footprint are identified as follows:

$\begin{bmatrix} {ab}_{c} \\ {bc}_{c} \\ {dc}_{c} \\ {ad}_{c} \end{bmatrix} = \begin{bmatrix} {\tan \left( {\alpha_{v}/2} \right)} & 0 & 1 \\ 0 & {\tan \left( {\alpha_{h}/2} \right)} & 1 \\ {- {\tan \left( {\alpha_{v}/2} \right)}} & 0 & 1 \\ 0 & {- {\tan \left( {\alpha_{h}/2} \right)}} & 1 \end{bmatrix}$

At step 306, each local coordinate is transformed to global inertial coordinates by multiplying the coordinate by pitch-roll-yaw rotation matrices [R] and [R_(c)], where

[R]=[R(θ)][R(φ)][R(ψ)]; and

[R _(c) ]=[R(θ_(c))][R(φ_(c))][R(ψ_(c))].

Thus,

A=a[R][R _(c)]

B=b[R][R _(c)]

C=c[R][R _(c)]

D=d[R][R _(c)]

I=i[R][R _(c)]

AB _(c) =ab _(c) [R][R _(c)]

BC _(c) =bc _(c) [R][R _(c)]

DC _(c) =dc _(c) [R][R _(c)]

AD _(c) =ad _(c) [R][R _(c)]

Rotational matrices are well known in the art, and are not described in detail here.

At step 308 the scaled coordinates of the sensor footprint are computed by scaling the inertial coordinates by the height (h) that AV 112 is flying above the ground (if target 116 is a ground target), or the height of AV 112 is flying above the target 116 (if target 116 is not necessarily on the ground). The footprint is calculated as follows:

$\quad\begin{matrix} {A_{g} = {A \times \frac{h}{A(3)}}} \\ {B_{g} = {B \times \frac{h}{B(3)}}} \\ {C_{g} = {C \times \frac{h}{C(3)}}} \\ {D_{g} = {D \times \frac{h}{D(3)}}} \\ {I_{g} = {I \times \frac{h}{I(3)}}} \\ {{AB}_{cg} = {{AB}_{c} \times \frac{h}{{AB}_{c}(3)}}} \\ {{BC}_{cg} = {{BC}_{c} \times \frac{h}{{BC}_{c}(3)}}} \\ {{DC}_{cg} = {{DC}_{c} \times \frac{h}{{DC}_{c}(3)}}} \\ {{AD}_{cg} = {{AD}_{c} \times \frac{h}{{AD}_{c}(3)}}} \end{matrix}$

After computing the various coordinates of sensor 114's sensor footprint, at step 310, a track metric ρ is calculated by (1) calculating a series of instantaneous normalized track metrics (ρ_(TR)) over time, and (2) calculating a weighted moving average of the instantaneous normalized track metrics.

ρ_(TR) is calculated using (1) the target's position relative to the center of the camera footprint (r_(target)) on the ground, (2) the distance from the center of the camera footprint to the side (e.g., [AB_(cg) BC_(cg) DC_(cg) AD_(cg)] of the footprint that is closest to the target (r_(side)), and (3) the distance from the center of the frame to the target (r_(t)). These positions are illustrated in FIG. 5, which is a diagram of a footprint that illustrates variables needed to calculate ρ_(TR). As shown in FIG. 5, the frame includes a center point, a target, r_(target), r_(t), r_(side), and the direction of the frame of motion.

In order to calculate ρ_(TR), the value of r_(target) and r_(side) is first calculated. r_(target) is calculated by using the following equation:

r _(target)=(ê _(ct) ·ê _(ce))r _(t)

where ê_(ct) and ê_(ce) are unit vectors along a line from the target to the center of the footprint and from the mid-point of the closest side to the center respectively. That is, ê_(ct) is the unit vector along r_(t), while ê_(ce) is the unit vector along r_(side).

r_(side) is calculated using the following equation:

$r_{side} = {\arg \; \min {\begin{matrix} {r_{{AB}_{cg}} - r_{target}} \\ {r_{{BC}_{cg}} - r_{target}} \\ {r_{{DC}_{cg}} - r_{target}} \\ {r_{{AD}_{cg}} - r_{target}} \end{matrix}}}$

where r_(AB) _(cg) -r_(AB) _(cg) is the distance from the center of the frame to the side of the frame.

After calculating r_(target) and r_(side), ρ_(TR) is calculated as follows:

$\rho_{TR} = \frac{r_{target}}{r_{side}}$

If ρ_(TR)=0, then the target 114 is directly over the center of the footprint. This is considered perfect tracking. If ρ_(TR)≧1, then AV 112 has lost track of target 114. Because AV 112 (and possibly target 116) are moving, ρ_(TR) should be calculated at regular time intervals (i.e., once every 10 ms), although ρ_(TR) could be calculated at random time intervals as well.

After calculating ρ_(TR), the track metric ρ is calculated as the weighted moving average of ρ_(TR), with more weight being placed on recently calculated values of ρ_(TR).

$\rho = {\begin{matrix} \left\lbrack b_{k} \right. & b_{k - 1} & \ldots & \left. b_{k - n} \right\rbrack \end{matrix}\begin{bmatrix} {\rho_{TR}(k)} \\ {\rho_{TR}\left( {k - 1} \right)} \\ \vdots \\ {\rho_{TR}\left( {k - n} \right)} \end{bmatrix}}$

where b_(i) are the weights, and

${\sum\limits_{i = 0}^{n}b_{k - i}} = 1$

where k is the sampling instant and n is the moving window over which the averaging is done. For example, if the sample time of the algorithm is t_(s) and the averaging is done over a time window of t seconds, then:

$n = \frac{t}{t_{s}}$

In addition to calculating ρ, the values of the first and second time derivatives of ρ ({dot over (ρ)},{umlaut over (ρ)}) may be calculated.

After obtaining ρ, at step 312, remote operator 118 is alerted when the value of ρ, {dot over (ρ)}, or {umlaut over (ρ)} exceeds a threshold value (ρ_(threshold), {dot over (ρ)}_(threshold), or {umlaut over (ρ)}_(threshold)). The threshold values may be a static or dynamic number. For example, the threshold for ρ may be set to be slightly less than one, and remote operator 118 will be alerted if ρ exceeds the threshold. For a fixed-wing AV with a non-steerable track sensor and minimum turn radius R_(min), flying with airspeed V, the threshold values for {dot over (ρ)} may be determined as follows:

{dot over (ρ)}_(threshold)=min((ê _(ct) ·ê _(ce))×V,V _(max))

Thus, remote operator 118 will be alerted if the value of {dot over (ρ)} is greater than the lesser of AV 112's maximum airspeed and its current airspeed resolved along the direction that the target is moving in the track sensor frame.

The value of {umlaut over (p)} may be determined as follows:

${\overset{¨}{\rho}}_{threshold} = {\frac{\left( {{\hat{e}}_{ct} \cdot {\hat{e}}_{ce}} \right)}{2\; R_{\min}}V^{2}}$

Therefore, the remote operator 118 will be alerted if {umlaut over (ρ)} is greater than the vehicle's acceleration along the direction of target movement in the frame.

Alerts sent to remote operator 118 in step 312 may be classified into categories. For example, the alert could indicate that AV 112 is about to lose track of target 116 and occurs when {umlaut over (p)} is slightly less than 1 (i.e., 0.9), and {dot over (ρ)} is positive. The alert could also indicate that target 116 is doing something significant, such as stopping suddenly, changing direction quickly, or engaging in aggressive or evasive motion maneuvers. Such an alert could be triggered if {dot over (ρ)} and {umlaut over (ρ)} both exceed their threshold values.

The present invention may be embodied in other specific forms without departing from its essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is therefore indicated by the appended claims rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope. 

1. A method comprising: identifying at least one coordinate of a footprint of a sensor, wherein the sensor is equipped on an aerial vehicle; computing a track metric as a function of how close a target is to the center of the footprint; comparing the track metric to a threshold value; and alerting a remote operator if the track metric falls outside a threshold value.
 2. The method of claim 1, wherein identifying at least one coordinate of the footprint comprises identifying coordinates of (i) vertices of the footprint and (ii) a center of the footprint.
 3. The method of claim 2, wherein identifying at least one coordinate of the footprint further comprises identifying coordinates of midpoints for each side of the footprint.
 4. The method of claim 3, wherein identifying at least one coordinate of the footprint further comprises identifying inertial coordinates of (i) the midpoints for each side of the footprint, (ii) the vertices of the footprint, and (iii) the center of the footprint.
 5. The method of claim 1, wherein the threshold value is based on a maximum airspeed of the aerial vehicle.
 6. The method of claim 1, wherein the threshold value is based on a minimum turn radius of the aerial vehicle.
 7. The method of claim 1, further comprising calculating a first time derivative and a second time derivative of the track metric.
 8. The method of claim 4, further comprising scaling the inertial coordinates of (i) the midpoints for each side of the footprint, (ii) the vertices of the footprint, and (iii) the center of the footprint, by a height the aerial vehicle is flying above the target.
 9. A system comprising: a communication interface; a processing unit; data storage; and program logic stored in the data storage and executable by the processing unit to (i) identify at least one coordinate of a footprint of a sensor, (ii) compute a track metric as a function of how close a target is to a center of the footprint, (iii) compare the track metric to a threshold value, and (iv) alert a remote operator if the track metric falls outside a threshold value.
 10. The system of claim 9, wherein the program logic is further executable to identify coordinates of (i) vertices of the footprint and (ii) a center of the footprint.
 11. The system of claim 10, wherein the program logic is further executable to identify coordinates of midpoints for each side of the footprint.
 12. The system of claim 11, wherein the program logic is further executable to identify inertial coordinates of (i) the midpoints for each side of the footprint, (ii) the vertices of the footprint, and (iii) the center of the footprint.
 13. The system of claim 9, wherein the threshold value is based on a maximum airspeed of an aerial vehicle.
 14. The system of claim 9, wherein the threshold value is based on a minimum turn radius of an aerial vehicle.
 15. A method comprising: determining a position of a target relative to the center of a sensor footprint; determining the distance from the center of the sensor footprint to a side of the sensor footprint that is closest to the target; calculating one or more instantaneous track metrics based on (i) the position of a target relative to the center of a sensor footprint; and (ii) the distance from the center of the sensor footprint to a side of the sensor footprint that is closest to the target; calculating a track metric as a weighted moving average of the instantaneous track metric; comparing the track metric to a threshold value; and alerting a remote operator if the track metric is outside of a threshold value.
 16. The method of claim 15, wherein weight values are used to determine the weighted moving average.
 17. The method of claim 16, wherein the weight values are provided by a remote operator.
 18. The method of claim 15, further comprising calculating a first time derivative of the track metric.
 19. The method of claim 18, further comprising calculating a second time derivative of the track metric.
 20. The method of claim 19, further comprising alerting the remote operator if the first and second time derivatives are outside a threshold value. 